# MinorBench: A hand-built benchmark for content-based risks for children

arXiv: https://arxiv.org/abs/2503.10242 · HTML: https://mlsanity.com/paper/2503.10242/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [ShieldGemma: Generative AI Content Moderation Based on Gemma](https://mlsanity.com/paper/2407.21772/) (2024)
- [The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks](https://mlsanity.com/paper/2502.08235/) (2025)
- [AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies](https://mlsanity.com/paper/2406.17864/) (2024)
- [LLMs and Childhood Safety: Identifying Risks and Proposing a Protection Framework for Safe Child-LLM Interaction](https://mlsanity.com/paper/2502.11242/) (2025)

## Built on by (most cited first, 9 of 9)

- [Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions](https://mlsanity.com/paper/2506.13510/) (2025)
- [Benchmarking the Pedagogical Knowledge of Large Language Models](https://mlsanity.com/paper/2506.18710/) (2025)
- [PAM: Training Policy-Aligned Moderation Filters at Scale](https://mlsanity.com/paper/2505.19766/) (2025)
- [Do You Feel Comfortable? Detecting Hidden Conversational Escalation in AI Chatbots](https://mlsanity.com/paper/2512.06193/) (2025)
- [CAREBench: A Child-Safety Risk Benchmark for Language Models](https://mlsanity.com/paper/2606.29685/) (2026)
- [CR4T: Rewrite-Based Guardrails for Adolescent LLM Safety](https://mlsanity.com/paper/2605.21609/) (2026)
- [The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models](https://mlsanity.com/paper/2605.25510/) (2026)
- [Beyond "I Can't Help With That": How Child Safety Experts Evaluate AI Chatbot Safety](https://mlsanity.com/paper/2608.07902/) (2026)
- [Understanding Parents' Complex Views of AI for Children's Pretend Play](https://mlsanity.com/paper/2609.39906/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are rapidly entering children's lives - through parent-driven adoption, schools, and peer networks - yet current AI ethics and safety research do not adequately address content-related risks specific to minors. In this paper, we highlight these gaps with a real-world case study of an LLM-based chatbot deployed in a middle school setting, revealing how students used and sometimes misused the system. Building on these findings, we propose a new taxonomy of content-based risks for minors and introduce MinorBench, an open-source benchmark designed to evaluate LLMs on their ability to refuse unsafe or inappropriate queries from children. We evaluate six prominent LLMs under different system prompts, demonstrating substantial variability in their child-safety compliance. Our results inform practical steps for more robust, child-focused safety mechanisms and underscore the urgency of tailoring AI systems to safeguard young users.